Gradient Boost

Before moving forward with the to-do list, let’s throw a Random Forest to it.

Gradient boost

For many reasons, Random Forest is usually a very good baseline model. In this particular case I started with the polynomial OLS as baseline model, just because it was so evident from the correlations that the relationship between temperature and consumption follows a polynomial shape. But let’s go back to a beloved RF.

Model Cards provide a framework for transparent, responsible reporting. 
 Use the vetiver `.qmd` Quarto template as a place to start, 
 with vetiver.model_card()
Writing pin:
Name: 'wd-gb'
Version: 20251122T162849Z-6a5a6

♻️  stepit 'gb_raw': is up-to-date. Using cached result for `strom.modelling.assess_model()` 2025-11-22 16:28:49

Metrics

	Single Split		CV
	train	test	test	train
MAE - Mean Absolute Error	1.409974	2.338275	2.303979	1.381000
MSE - Mean Squared Error	3.723677	21.094079	23.726213	3.632431
RMSE - Root Mean Squared Error	1.929683	4.592829	3.941234	1.902960
R2 - Coefficient of Determination	0.960355	0.765333	0.384288	0.962644
MAPE - Mean Absolute Percentage Error	0.137213	0.226350	0.171635	0.127905
EVS - Explained Variance Score	0.960355	0.772304	0.432298	0.962644
MeAE - Median Absolute Error	1.028508	1.192214	1.476596	0.995582
D2 - D2 Absolute Error Score	0.796192	0.655386	0.337103	0.804585
Pinball - Mean Pinball Loss	0.704987	1.169138	1.151989	0.690500

Scatter plot matrix

Observed vs. Predicted and Residuals vs. Predicted

Check for …

check the residuals to assess the goodness of fit.

white noise or is there a pattern?
heteroscedasticity?
non-linearity?

Normality of Residuals:

Check for …

Are residuals normally distributed?

Leverage

Scale-Location plot

Residuals Autocorrelation Plot

Residuals vs Time

Again, overfits a lot.

Parameter: param_model__learning_rate

/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['0.1' '0.1' '0.1' ... '0.1' '0.1' '0.1']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['48' '48' '48' ... '48' '48' '48']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['60' '60' '60' ... '60' '60' '60']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '1' '1' ... '1' '1' '1']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.

Parameter: param_model__max_depth

/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['0.1' '0.1' '0.1' ... '0.1' '0.1' '0.1']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['48' '48' '48' ... '48' '48' '48']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['60' '60' '60' ... '60' '60' '60']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '1' '1' ... '1' '1' '1']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.

Parameter: param_model__min_samples_leaf

/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['0.1' '0.1' '0.1' ... '0.1' '0.1' '0.1']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['48' '48' '48' ... '48' '48' '48']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['60' '60' '60' ... '60' '60' '60']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '1' '1' ... '1' '1' '1']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.

Parameter: param_model__min_samples_split

/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['0.1' '0.1' '0.1' ... '0.1' '0.1' '0.1']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['48' '48' '48' ... '48' '48' '48']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['60' '60' '60' ... '60' '60' '60']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '1' '1' ... '1' '1' '1']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.

Parameter: param_model__n_estimators

/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['0.1' '0.1' '0.1' ... '0.1' '0.1' '0.1']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['48' '48' '48' ... '48' '48' '48']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['60' '60' '60' ... '60' '60' '60']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '1' '1' ... '1' '1' '1']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.

Parameter: param_model__subsample

/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['0.1' '0.1' '0.1' ... '0.1' '0.1' '0.1']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['48' '48' '48' ... '48' '48' '48']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['60' '60' '60' ... '60' '60' '60']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '1' '1' ... '1' '1' '1']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.

Parameter: param_vars__columns

/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['0.1' '0.1' '0.1' ... '0.1' '0.1' '0.1']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5' '5' '5' ... '5' '5' '5']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['48' '48' '48' ... '48' '48' '48']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['60' '60' '60' ... '60' '60' '60']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
/home/runner/work/strom/strom/src/strom/strom.py:837: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '1' '1' ... '1' '1' '1']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.

Best model

{'model__learning_rate': 0.1,
 'model__max_depth': 5,
 'model__min_samples_leaf': 5,
 'model__min_samples_split': 48,
 'model__n_estimators': 60,
 'model__subsample': 1,
 'vars__columns': ['rf_tu_mean', 'vp_std_mean']}

♻️  stepit 'gb_tuned': is up-to-date. Using cached result for `strom.modelling.assess_model()` 2025-11-22 16:28:56

Metrics

	Single Split		CV
	train	test	test	train
MAE - Mean Absolute Error	1.563379	2.374218	2.208940	1.548676
MSE - Mean Squared Error	6.409281	21.100475	22.067249	6.006925
RMSE - Root Mean Squared Error	2.531656	4.593525	3.773892	2.440925
R2 - Coefficient of Determination	0.931762	0.765262	0.450495	0.938437
MAPE - Mean Absolute Percentage Error	0.141916	0.237917	0.170104	0.134164
EVS - Explained Variance Score	0.931762	0.771610	0.503243	0.938437
MeAE - Median Absolute Error	1.028037	1.231788	1.418443	1.030482
D2 - D2 Absolute Error Score	0.774018	0.650089	0.352238	0.780897
Pinball - Mean Pinball Loss	0.781690	1.187109	1.104470	0.774338

Scatter plot matrix

Observed vs. Predicted and Residuals vs. Predicted

Check for …

check the residuals to assess the goodness of fit.

white noise or is there a pattern?
heteroscedasticity?
non-linearity?

Normality of Residuals:

Check for …

Are residuals normally distributed?

Leverage

Scale-Location plot

Residuals Autocorrelation Plot

Residuals vs Time

Compare vanilla vs. tuned

Cross-validation messages

♻️  stepit 'cross_validate_pipe': is up-to-date. Using cached result for `strom.modelling.cross_validate_pipe()` 2025-11-22 16:29:00

♻️  stepit 'cross_validate_pipe': is up-to-date. Using cached result for `strom.modelling.cross_validate_pipe()` 2025-11-22 16:29:00

Metrics

Single split

Metrics based on the test set of the single split

Cross validation

Predictions, residuals, observed

Time vs. Residuals

Model details

Pipeline(steps=[('vars',
                 ColumnSelector(columns=['tt_tu_mean', 'rf_tu_mean', 'td_mean',
                                         'vp_std_mean', 'tf_std_mean'])),
                ('model', GradientBoostingRegressor(random_state=7))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Pipeline(steps=[('vars', ColumnSelector(columns=['rf_tu_mean', 'vp_std_mean'])),
                ('model',
                 GradientBoostingRegressor(max_depth=5, min_samples_leaf=5,
                                           min_samples_split=48,
                                           n_estimators=60, random_state=7,
                                           subsample=1))])

	steps	[('vars', ...), ('model', ...)]
	transform_input	None
	memory	None
	verbose	False

	loss	'squared_error'
	learning_rate	0.1
	n_estimators	100
	subsample	1.0
	criterion	'friedman_mse'
	min_samples_split	2
	min_samples_leaf	1
	min_weight_fraction_leaf	0.0
	max_depth	3
	min_impurity_decrease	0.0
	init	None
	random_state	7
	max_features	None
	alpha	0.9
	verbose	0
	max_leaf_nodes	None
	warm_start	False
	validation_fraction	0.1
	n_iter_no_change	None
	tol	0.0001
	ccp_alpha	0.0

	steps	[('vars', ...), ('model', ...)]
	transform_input	None
	memory	None
	verbose	False

	loss	'squared_error'
	learning_rate	0.1
	n_estimators	60
	subsample	1
	criterion	'friedman_mse'
	min_samples_split	48
	min_samples_leaf	5
	min_weight_fraction_leaf	0.0
	max_depth	5
	min_impurity_decrease	0.0
	init	None
	random_state	7
	max_features	None
	alpha	0.9
	verbose	0
	max_leaf_nodes	None
	warm_start	False
	validation_fraction	0.1
	n_iter_no_change	None
	tol	0.0001
	ccp_alpha	0.0

Gradient Boost

Gradient boost

Metrics

Scatter plot matrix

Observed vs. Predicted and Residuals vs. Predicted

Normality of Residuals:

Leverage

Scale-Location plot

Residuals Autocorrelation Plot

Residuals vs Time

Parameter: param_model__learning_rate

Parameter: param_model__max_depth

Parameter: param_model__min_samples_leaf

Parameter: param_model__min_samples_split

Parameter: param_model__n_estimators

Parameter: param_model__subsample

Parameter: param_vars__columns

Best model

Metrics

Scatter plot matrix

Observed vs. Predicted and Residuals vs. Predicted

Normality of Residuals:

Leverage

Scale-Location plot

Residuals Autocorrelation Plot

Residuals vs Time

Compare vanilla vs. tuned

Metrics

Single split

Cross validation

Predictions, residuals, observed

Time vs. Predicted and Observed

Time vs. Residuals

Model details

TODOs